Identification and control of dynamic systems using recurrent fuzzy neural networks

By Ching-Hung Lee, Ching-Cheng Teng. IEEE transactions on fuzzy systems, 2000

635 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/51317b6082322a96b4570818b7a5ec8b2e330f2f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chin-Teng Lin
2. Yang-Yin Lin
3. Jyh-Yeong Chang
4. Chia-Feng Juang
5. J. Urgen Schmidhuber
6. Yaochu Jin
7. Ran Cheng
8. Zahra Beheshti

In Control and Systems Engineering

1. Chin-Teng Lin
2. Yang-Yin Lin
3. Jyh-Yeong Chang
4. Chia-Feng Juang

Citations in the corpus

• A hybrid of genetic algorithm and particle swarm optimization for recurrent network design (2004, background): “In contrast, for problems that include concurrent spatial and temporal mapping, the performance of recurrent fuzzy network has shown to outperform that of recurrent neural networks [26], [27].”
• A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms (2002, methodology): “In [23], RFNN is applied to the same problem, and the same network size and training approach are simulated in the paper.”
• Identification and Prediction of Dynamic Systems Using an Interactively Recurrent Self-Evolving Fuzzy Neural Network (2013, background): “The other approach of recurrent FNNs [10]–[14] uses feedback loops from internal state variables as its recurrence structure.”